Decision Making and Set Shifting Impairments Are Associated With Distinct Symptom Dimensions in Obsessive–Compulsive Disorder

Decision Making and Set Shifting Impairments Are Associated With Distinct Symptom Dimensions in Obsessive–Compulsive Disorder
[Article]
Lawrence, Natalia S.1,4; Wooderson, Sarah1; Mataix-Cols, David2; David, Rhodri3; Speckens, Anne3; Phillips, Mary L.1
1Section of Neuroscience and Emotion, Division of Psychological Medicine and Psychiatry, Institute of Psychiatry, King's College London, London, England
2Section of Neuroscience and Emotion, Division of Psychological Medicine and Psychiatry, Institute of Psychiatry, and Department of Psychology, Institute of Psychiatry, King's College London, London, England
3Department of Psychology, Institute of Psychiatry, King's College London, London, England
4Correspondence concerning this article should be addressed to Natalia S. Lawrence, P.O. Box 69, Section of Neuroscience and Emotion, Division of Psychological Medicine and Psychiatry, Institute of Psychiatry, King's College London, De Crespigny Park, London, SE5 8AF England. E-mail: n.lawrence@iop.kcl.ac.uk
Anne Speckens is now at the Department of Psychiatry, University Medical Centre St. Radboud, Nijmegen, the Netherlands.
This study was funded by Wellcome Trust Grant 064846 to David Mataix-Cols, Anne Speckens, and Mary L. Phillips. We thank Jeffrey Dalton for his technical support.
Received Date: February 11, 2005; Revised Date: February 1, 2006; Accepted Date: February 27, 2006
Abstract
Obsessive–compulsive disorder (OCD) is clinically heterogeneous. The authors examined how specific OCD symptom dimensions were related to neuropsychological functions using multiple regression analyses. A total of 39 OCD patients and 40 controls completed the Iowa Gambling Task (IGT; A. Bechara, A. R. Damasio, H. Damasio, & S. W. Anderson, 1994), which is a test of decision making, and the Wisconsin Card Sorting Test (R. K. Heaton, 1981), which is a test of set shifting. OCD patients and controls showed comparable decision making. However, patients with prominent hoarding symptoms showed impaired decision making on the IGT as well as reduced skin conductance responses. OCD patients had poorer set shifting abilities than controls, and symmetry/ordering symptoms were negatively associated with set shifting. These results help explain previous inconsistent findings in neuropsychological research in OCD and support recent neuroimaging data showing dissociable neural mechanisms involved in mediating the different OCD symptom dimensions.
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Obsessive–compulsive disorder (OCD) is clinically heterogeneous. Factor analytic studies have consistently identified distinct but potentially overlapping symptom dimensions within OCD, which can coexist in the same patients and are stable over time (Mataix-Cols, Rauch, et al., 2002). At least four such dimensions have been identified: contamination/washing, obsessions/checking, hoarding, and symmetry/ordering (Mataix-Cols, Rosario-Campos, & Leckman, 2005). Mounting evidence suggests that each of these dimensions can be distinguished on the basis of their associated phenomenology, comorbidity, and patterns of genetic transmission (Mataix-Cols et al., 2005). Furthermore, these dimensions can be predictive of clinical outcome with various treatments, with hoarding symptoms in particular predicting a poor response to both pharmacotherapy and behavior therapy (Alonso et al., 2001; Black, Monahan, Gable, Blum, Clancy, & Baker, 1998; Mataix-Cols, Marks, Greist, Kobak, & Baer, 2002; Mataix-Cols, Rauch, Manzo, Jenike, & Baer, 1999; Saxena et al., 2002; Winsberg, Cassic, & Koran, 1999). More recent studies have examined the neural correlates of these dimensions (Mataix-Cols et al., 2004; Rauch et al., 1998; Saxena et al., 2004) and have demonstrated distinct, albeit partially overlapping, patterns of brain activity in relation to each symptom dimension. The neuropsychological profile associated with these symptom dimensions remains to be examined systematically, hence, the current study.
Here, we focus on two aspects of neuropsychological function: (a) decision making, measured by performance on the Iowa Gambling Task (IGT; Bechara, Damasio, Damasio, & Anderson, 1994), and (b) attentional set shifting, measured with the Wisconsin Card Sorting Task (WCST; R. K. Heaton, 1981). Performance on these tasks is believed to be particularly sensitive to ventromedial and dorsolateral prefrontal cortex (PFC) function, respectively (Bechara, Damasio, Tranel, & Anderson, 1998; Milner, 1963). However, it should be noted that as both tests involve multiple cognitive functions (such as attention, working memory, associative learning, and response inhibition), more widespread functional localizations for both tasks have been reported (for discussion of the WCST and IGT, respectively, see Anderson, Damasio, Jones, & Tranel, 1991; Clark, Manes, Antoun, Sahakian, & Robbins, 2003). Impaired performance on both the IGT and WCST has previously been demonstrated in OCD patients, but findings are inconsistent (Cavallaro et al., 2003; Cavedini, Riboldi, D'Annucci, et al., 2002; Kuelz, Hohagen, & Voderholzer, 2004; Nielen, Veltman, De Jong, Mulder, & Den Boer, 2002), which may be partly due to the heterogeneity of OCD. The IGT, as a laboratory measure of decision making (Bechara et al., 1994), is of particular interest because indecisiveness is a prominent feature of patients with OCD and in particular those with hoarding symptoms (Steketee & Frost, 2003). Although previous studies are inconsistent in reporting deficits on the IGT in OCD patients, they do suggest poorer IGT performance in patients who fail to respond to pharmacotherapy (Cavedini, Riboldi, D'Annucci, et al., 2002) and in those with more severe OCD and greater anxiety (Nielen et al., 2002). Other populations with putative abnormalities in ventromedial PFC function also show deficits on the IGT—namely, individuals with ventromedial PFC lesions (Bechara et al., 1994; Bechara, Tranel, Damasio, & Damasio, 1996), pathological gamblers (Cavedini, Riboldi, Keller, D'Annucci, & Bellodi, 2002), psychopathic individuals (Mitchell, Colledge, Leonard, & Blair, 2002; Van Honk, Hermans, Putman, Montagne, & Schutter, 2002), and substance-dependent individuals (Bechara & Damasio, 2002; Grant, Contoreggi, & London, 2000). Continued risk taking on the IGT has often been accompanied by a reduced ability to generate skin conductance responses (SCRs) prior to choosing cards from high-risk decks (Bechara & Damasio, 2002; Bechara, Damasio, Tranel, & Damasio, 1997; Bechara et al., 1996) or in response to punishment (Bechara & Damasio, 2002; Suzuki, Hirota, Takasawa, & Shigemasu, 2003), suggesting that these autonomic signals (somatic markers) provide useful information that guides decision making (Bechara & Damasio, 2002; Damasio, 1994). We sought to clarify whether symptom dimensions that were more associated with ventromedial PFC activation during symptom provocation (i.e., washing and hoarding; Mataix-Cols et al., 2004) showed greater risk taking and lower SCR during IGT performance.
The WCST, a measure of attentional set shifting, has been associated with distinct underlying cognitive and neural mechanisms from the IGT, and as such, intact WCST performance has been shown in various populations displaying deficits on the IGT (Bechara et al., 1996, 1998; Grant et al., 2000; Overman et al., 2004), including patients with OCD (Cavallaro et al., 2003). Neuropsychological studies have reported set shifting deficits on the WCST in OCD patients, but such findings are inconsistent (for a review, see Kuelz et al., 2004), and deficits can often be attributed to the increased depression in OCD patients (Basso, Bornstein, Carona, & Morton, 2001; Moritz et al., 2001).
We aimed to determine the extent to which different OCD symptom dimensions were associated with impaired decision making and set shifting abilities using the IGT and WCST, respectively. We predicted that OCD patients with predominant symptom dimensions associated with abnormal ventromedial PFC activity—namely, contamination/washing and hoarding—would be most impaired in decision making and would demonstrate accompanying abnormalities in SCR. Data to date also allowed us to predict that OCD patients with predominant symptom dimensions associated with abnormal dorsal prefrontal cortical and striatal activity—namely, checking/obsessions (Mataix-Cols et al., 2004)—would be less impaired on the IGT and would show greater deficits in set shifting on the WCST.
Method
Participants
A total of 39 OCD patients (20 men, 19 women) who were at various stages of treatment were recruited consecutively from two specialized cognitive–behavioral therapy clinics in London, England. The sample contained 26 inpatients and 13 outpatients. Axis I and II diagnoses were made according to Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM–IV; American Psychiatric Association, 1994) criteria by a psychiatrist (Anne Speckens) or nurse therapist using the Structured Clinical Interview for DSM–IV (First, Gibbon, Spitzer, Williams, & Smith, 1995; First, Spitzer, Gibbon, & Williams, 1995). Patients with comorbid diagnoses were not excluded provided that OCD was the main problem for which treatment was sought. Exclusion criteria were brain injury, any neurological condition, psychosis, or substance abuse. A total of 40 healthy volunteers (20 men, 20 women) of similar demographic characteristics were recruited among ancillary staff at the Institute of Psychiatry (London, England) and from the local community. They reported no history of neurological or psychiatric disorder by clinical screen and were unmedicated at the time of the study. The Ethics Committee (Research) of the Maudsley Hospital and Institute of Psychiatry (London, England) approved the study protocol, and all participants signed an informed consent form prior to their participation. Participants were paid £25 (approximately $46) for their participation in the study.
The patients' mean illness duration was 20.7 years (SD = 12; range = 3–53). OCD severity was in the moderate-to-severe range (Yale–Brown Obsessive–Compulsive Scale [Y-BOCS; Goodman et al., 1989] total: M = 25.13, SD = 6.9; obsessions: M = 12.67, SD = 3.7; compulsions: M = 12.46, SD = 3.7). Of the patients, 29 (74%) had one or more comorbid Axis I or Axis II disorders. Additional Axis I diagnoses were major depressive disorder (n = 8), dysthymic disorder (n = 7), social phobia (n = 2), specific phobia, panic disorder, panic disorder with agoraphobia, posttraumatic stress disorder, generalized anxiety disorder, hypochondriasis, and body dysmorphic disorder (each, n = 1). Comorbid personality disorders were avoidant (n = 10), obsessive–compulsive (n = 6), depressive (n = 5), dependent (n = 3), paranoid (n = 3), negativistic (n = 2), borderline (n = 2), and schizoid (n = 1). Most patients (n = 30; 77%) were on medication at the time of the study: citalopram (n = 4; mean dose = 22.5 mg), clomipramine (n = 4; mean dose = 163 mg), fluoxetine (n = 8; mean dose = 44 mg), fluvoxamine (n = 3; mean dose = 133 mg), paroxetine (n = 8; mean dose = 44 mg), venlafaxine (n = 2; mean dose = 131 mg), and lithium (n = 1; mean dose = 1,000 mg). Additional medications included diazepam (n = 2; mean dose = 6 mg), zopiclone (n = 2; mean dose = 5.6 mg), buspirone (n = 1; mean dose = 10 mg), and amitriptyline (n = 1; mean dose = 25 mg).
Measures of Symptoms
In the OCD group, severity and types of OCD symptom were assessed with the Y-BOCS and Symptom Checklist (Goodman et al., 1989) administered on the day of testing. In both groups, symptom dimension scores were obtained with a revised version of the Obsessive–Compulsive Inventory—Revised (OCI–R; Foa et al., 2002). In addition, all participants completed the Savings Inventory—Revised (SI–R; Frost, Steketee, & Grisham, 2004), which is a 23-item scale that measures hoarding symptoms. We assessed depression using the Beck Depression Inventory (Beck, Ward, Mendelson, Mock, & Erbaugh, 1961), and we administered the State subscale of the State–Trait Anxiety Inventory (Spielberger, 1983) immediately before testing.
Neuropsychological Tests
All participants performed computerized versions of the IGT and the WCST as part of a larger battery of neuropsychological tests that took about 120 min to complete. The order of test administration was pseudorandomized across participants. Participants were also administered the National Adult Reading Test (NART; Nelson, 1982) to provide an estimate of (premorbid) verbal IQ. The computerized version of the IGT (Bechara, Tranel, & Damasio, 2000) was reprogrammed locally to present monetary gains and losses in British pound Sterling and to enable online measurement of SCR. The IGT (Bechara et al., 1994) simulates real-life decision making by testing the ability of participants to sacrifice immediate rewards in favor of long-term gain. Briefly, participants must select 100 cards, one at a time, from four identical-looking decks of cards, labeled A, B, C, and D. Participants are told that every time they pick a card they will win some money, but occasionally they will also lose some money. They can select cards from any deck, and the goal of the game is to win as much money as possible. All participants begin with a £2000 (approximately $3,666) loan to play the game, and wins and losses are tracked on screen. Decks A and B are associated with large gains (between £80 [approximately $147] and £150 [approximately $275]) and occasional large losses (£150–£2000 [approximately $275–$3,666]), whereas Decks C and D provide smaller gains (£40–£85 [approximately $73–$156]) and occasional small losses (£25–£325 [approximately $46–$596]). Decks A and B are referred to as “disadvantageous” because they lead to a net loss over time, whereas Decks C and D are “advantageous” because they lead to a net gain. Task performance is measured by calculating the number of cards picked from advantageous decks (C + D) minus the number of cards picked from disadvantageous decks (A + B) in each block of 20 card selections. Healthy controls usually show an improvement in performance over the five blocks of 20 card selections, indicating that over time they are learning to avoid the disadvantageous decks associated with larger losses. Participants were not paid any extra real money depending on their performance in this task. Following task completion, participants were asked to describe any strategy they had used, and then they were asked whether they had picked more cards from any particular deck(s), whether they had avoided any particular deck(s), and why they had done so. Responses were graded as “1” if participants explained that cards from Deck A (and/or B) resulted in a net loss, whereas cards from Deck C (and/or D) produced a net gain and were graded as “0” if they gave another (incorrect) response. These questions aimed to determine which participants had developed explicit awareness of the rules for winning the IGT. The presence of explicit awareness can help characterize the nature of IGT deficits; for example, participants who show explicit awareness of the rules and normal autonomic arousal during task performance but continue to choose from the disadvantageous decks are thought to be “conscious” or deliberate risk takers (Bechara & Damasio, 2002). Data from 2 control participants who had prior experience of the task were excluded from the analysis.
We measured SCRs using two silver chloride electrodes (SLE Limited, Surrey, England) applied to the palmar surface of the middle phalanx of the index finger and the middle phalanx of the middle finger of the nondominant hand. The electrodes were connected to a PSYLAB SC5 25-bit digital skin conductance amplifier, connected via a serial cable to a PSYLAB stand-alone-monitor unit (Contact Precision Instruments, London, England). The stand-alone-monitor unit relayed SCR data to a PSYLAB host computer running PSYLAB7 software, which logged every card pick on the SCR trace. To ensure adequate time for measurement of SCR, we imposed a minimum 6-s interstimulus interval between card picks, following the method of Bechara, Damasio, Damasio, and Lee (1999). This timing enables SCR to be divided into a “response” and an “anticipatory” phase for each card selection, with the 5 s following each card pick constituting the response phase, and the remaining time (variable but at least 1 s) before the next card selection constituting the anticipatory phase. These SCR phases are typically analyzed separately (Bechara et al., 1999). We analyzed SCR data using locally developed software (SCAnalyse, Version 4; J. Dalton, King's College London, London). SCR fluctuations with a minimum rise time of 500 ms and a minimum amplitude of 0.01 µS were automatically detected with our software. The number of fluctuations in each response and anticipatory time window for each card selection was measured. When there was no fluctuation associated with a card selection, “0” was recorded. The mean number of response SCRs was then calculated separately for wins and for losses from the disadvantageous decks (A, B) and the advantageous decks (C, D). The mean number of anticipatory SCRs was calculated separately for the disadvantageous and advantageous decks—the reinforcement is irrelevant for anticipatory SCR, as participants do not know whether they will win or lose when deciding which card to pick. It should be noted that because many participants failed to generate anticipatory or response SCRs in relation to every card selected the mean number of fluctuations in each condition for each group was below 1.0. Because of technical faults and the reluctance of 1 patient to wear electrodes, complete SCR data were collected from 28 OCD patients and 28 healthy controls. Of these, 5 OCD patients and 4 controls were classed as “nonresponders” (showed only 0–5 SCR fluctuations over all 100 events; the mean for the remaining sample was 100 fluctuations) and were excluded from further analysis, leaving SCR data for 23 OCD patients and 24 controls.
A computerized version of the WCST (Heaton, 1981) was administered to all participants. Participants were presented with individual cards at the bottom of a screen, which they had to match to one of four cards at the top of the screen using any criteria they chose. The four top cards remained constant throughout the task and enabled matching on the basis of shape, color, or number of stimuli. Participants were provided with onscreen feedback of “Right” when they had matched correctly and “Wrong” when they had matched incorrectly. After 10 consecutive correct responses, the rule changed so that another feature became the correct matching criteria. The task continued until six changes in criteria (categories) or 100 trials had been completed. Responses measured were as follows: the number of categories completed (of six), the number of perseverative errors (cards matched according to an old rule after a change in rule), and the number of nonperseverative errors (e.g., random errors until the new rule has been learned or errors in the middle of a category). Data from 1 OCD patient and 1 healthy control were lost because of technical problems.
Statistical Analyses
All statistical analyses were carried out with the Statistical Package for the Social Sciences (SPSS) Version 12.0 for Windows (SPSS Inc., Chicago, Illinois). We compared demographic variables for OCD patients and healthy controls using one-way analysis of variance (ANOVA) for age, education, and verbal IQ (estimated with the NART). We compared gender distribution using chi-square. We analyzed scores on the scales measuring OCD symptoms and mood (other than the State–Trait Anxiety Inventory) using nonparametric Mann–Whitney U tests because of unequal variance in the two groups. Standardized effect sizes (Cohen's d) for the between-groups comparisons are reported, with d = 0.2 regarded as a small effect, d = 0.5 as a medium effect, and d = 0.8 as a large effect (Cohen, 1988). We compared performance on the gambling task using repeated-measures ANOVA, with group and gender as between-groups factors, covarying for depression. Gender was included as a factor because it has previously been shown to influence IGT performance, with men performing better than women (Overman et al., 2004; Reavis & Overman, 2001). Between-groups analyses covaried for depression because this has previously been shown to account for some reported neuropsychological impairments in OCD (Basso et al., 2001; Moritz et al., 2001). Education and verbal IQ were not entered as covariates in the analysis of IGT results because they were unrelated to IGT performance: in the whole sample (N = 77), years of education (r = .13, p = .25), verbal IQ (r = .16, p = .17); in OCD patients (n = 39), years of education (r = .22, p = .17), verbal IQ (r = .14, p = .40). These nonsignificant correlations replicate several previous reports of a lack of association between education or IQ and IGT performance (Bechara et al., 1994; Cavallaro et al., 2003; Cavedini, Riboldi, D'Annucci, et al., 2002; Grant et al., 2000). We assessed differences in SCR during the gambling task using repeated-measures ANOVA with deck (advantageous vs. disadvantageous) and reinforcement (win vs. loss) as repeated measures, and group and gender as between-groups factors. This analysis also covaried for depression. Effect sizes for all significant main effects and interactions in repeated-measures ANOVAs are reported with partial eta squared ([eta]p2), which describes the proportion of total variability attributable to that factor. A [eta]p2 of .01 denotes a small effect size, .059 a medium effect size, and >= .138 a large effect size (Cohen, 1988). For post hoc Fisher's least significant difference (LSD) tests comparing factors contributing to a main effect, we have reported mean differences and 95% confidence intervals (CIs). We assessed performance on the WCST using the Mann–Whitney U test because of nonnormality of the data and the unequal variance in the two groups.
We explored relationships between performance on the neuropsychological tests and OCD symptom dimensions within OCD patients using stepwise linear regression. In these regressions, the dependent variables were the neuropsychological tests. Independent variables were all OCD symptom dimension scores (derived from the OCI–R). The OCI–R rather than the Y-BOCS was used because it measures OCD symptoms dimensionally. Symptom severity (Y-BOCS total), state anxiety, depression, age, education, and verbal IQ were also forced into the models to control for their effect. The potential confound of multicollinearity between independent variables was assessed by examining each variable's tolerance (the proportion of a variable's variance not accounted for by other independent variables in the model). High-tolerance values indicate a lack of multicollinearity. Because of the relatively large number of independent variables in the regression analyses, these should be viewed as exploratory.
Results
Demographic characteristics of the sample are summarized in Table 1. There were no significant differences between OCD patients and controls on any demographic variable apart from years of education completed; controls had completed a mean of 14.65 years of education, whereas patients had completed a mean of 13.15 years. This difference can be explained by the relatively early onset of OCD in this sample, with 29 patients (74%) reporting onset of illness during childhood or adolescence. The mean estimated verbal IQ (obtained with the NART) did not differ between patients and controls, suggesting that the difference in years of education was not linked to differences in verbal IQ. OCD patients scored more highly than controls on scales measuring OCD symptoms (OCI–R, SI–R), state anxiety, and depression.

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Table 1 Demographic and Clinical Characteristics of Obsessive–Compulsive Disorder (OCD) Patients and Healthy Control Participants
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Types of OCD Symptoms
All patients reported more than one current symptom type at the time of testing. The frequencies (percentage) of the main categories of the Y-BOCS Symptom Checklist were as follows: (a) Obsessions: aggressive = 23 (59%), contamination = 28 (72%), sexual = 12 (31%), hoarding/saving = 17 (44%), religious = 18 (46%), symmetry = 25 (64%), somatic = 17 (44%); (b) Compulsions: washing = 27 (69%), checking = 30 (77%), repeating = 28 (72%), counting = 10 (26%), ordering = 15 (38%), hoarding = 15 (38%).
Decision Making on the Gambling Task
Repeated-measures ANOVA was carried out with the net score per five blocks of 20 card selections as the repeated measures (to assess learning), and group and gender as the between-subjects variables, covarying for depression. Results show a significant effect of block, F(4, 69) = 4.94, p < .01, [eta]p2 = .22, indicating that performance improved over time as expected. In addition, there was a highly significant effect of gender on task performance, F(1, 72) = 16.01, p < .01, [eta]p2 = .18, but no effect of group, F(1, 72) = 0.01, p = .93, [eta]p2 = 0, or any significant interactions. Adding education as an additional covariate in the analysis had no effect other than reducing the effect of block, F(4, 68) = 0.10, p = .98, [eta]p2 < .01. Our findings therefore agree with those of Nielen et al. (2002), suggesting no overall deficit on the IGT in OCD patients relative to controls.
To examine whether participants were explicitly aware of any strategies used to win money on the task, we asked most participants (83%; equal numbers of OCD and control, men and women) to describe their strategy after they had completed the task (see Methods section for details). Results indicate that 69% of men and 34% of women guessed the correct (or partially correct) strategy; a distribution that was significantly different between genders, chi-square (df, 1) = 7.57, p < .01. Furthermore, explicit awareness of the “rule” was associated with better performance, as shown by the main effect of rule awareness on IGT score over the five blocks, F(1, 62) = 67.41, p < .01, [eta]p2 = .52.
SCRs During the Gambling Task
Repeated-measures ANOVA on the number of anticipatory SCRs, covarying for depression, indicated no significant effects of group, gender, deck, or any interactions. Repeated-measures ANOVA on the number of response SCRs, with reinforcement (win vs. loss) and decks picked (advantageous vs. disadvantageous) as the repeated measures, covarying for depression, showed a main effect of reinforcement, F(1, 42) = 17.68, p < .01, [eta]p2 = .30; loss was greater than win, but there was no effect of deck, group, gender, or any interactions. There were therefore no group differences in the number of SCR fluctuations during performance of the IGT. The finding of increased SCR to losses is consistent with previous studies indicating that SCR is proportional to the affective value of stimuli—increasing with larger reinforcements (especially negative) and less frequent events; that is, infrequent, large losses produce the largest SCR (Bechara et al., 1999; Suzuki et al., 2003).
Relationship Between Decision Making and OCD Symptoms
The results of the exploratory multiple regression analysis, examining relationships between OCD symptoms (as measured by the OCI–R), illness severity, depression, anxiety, age, education, verbal IQ, and total net score on the IGT (cards picked from [C + D] - [A + B]) suggest that hoarding and washing but not other types of symptom were independently negatively associated with IGT performance (R2 = .35; hoarding, [beta] = -.49, p < .01; washing, [beta] = -.35, p < .02). No other variables (illness severity, state anxiety, depression, age, education, verbal IQ) were associated with performance (see Table 2). The negative association between hoarding and performance remained significant when men and women were analyzed separately (for men, R2 = .26; [beta] = -.51, p < .03; for women, R2 = .27; [beta] = -.52, p < .04), whereas the negative association with washing did not remain (for men, [beta] = -.16, p = .45; for women, [beta] = -.29, p = .20). These findings suggest that hoarding is strongly negatively associated with gambling task performance, and washing symptoms also show some negative association with performance on the IGT, albeit to a lesser extent.

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Table 2 Standardized Regression Coefficients for Each Predictor Variable in the Multiple Regression Analyses Examining the Effect of OCD Symptoms, Mood and Sociodemographic Variables on IGT, and WCST Performance in OCD Patients
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As performance on the IGT was strongly negatively related to hoarding symptoms, we examined whether there were any differences in IGT performance and task-related SCRs between controls and patients with and without hoarding symptoms. This reanalysis aimed to reconcile the seemingly conflicting findings that IGT performance did not differ between OCD patients and controls and yet was strongly related to hoarding symptoms in OCD patients. We wished to determine whether a particular subgroup of OCD patients, rather than OCD patients per se, were impaired on the IGT. To this end, we divided OCD patients into those with high and low hoarding symptoms. The patients with high hoarding (n = 10) endorsed hoarding on the Y-BOCS symptom checklist and scored above the OCD group mean and median on both the OCI–R Hoarding subscale and the SI–R. These patients therefore consistently rated hoarding as one of their symptoms, but hoarding was not necessarily their only or main symptom. None of the patients reported hoarding as their only symptom on the Y-BOCS Symptom Checklist. The 10 high hoarding participants had similar demographics, illness severity, anxiety, depression, comorbidity, medication, and gender distribution to the group of OCD patients with low hoarding. The mean score of the 10 high hoarding participants on the 23-item SI–R was 50.1 (SD = 13.7), which is similar to the mean for groups of hoarders in other studies (Frost et al., 2004) and was higher than the score in patients with low hoarding (n = 29; M = 17.5, SD = 15.6) and controls (n = 38; M = 14.3, SD = 13.3). A between-groups ANOVA confirmed the difference in SI–R score; overall between-groups difference, F(2, 76) = 28.90, p < .01, [eta]p2 = .45, with the high hoarding group scoring significantly higher than both controls (p < .01, mean difference = 35.78, CI = 26.25, 45.31) and OCD patients with low-hoarding symptoms (p < .01, mean difference = 32.6, CI = 22.65, 42.55).
A repeated-measures ANOVA on performance data (net score per block of 20 card selections) as the repeated measure and group (controls, patients with high hoarding, and patients with low hoarding) and gender as the between-groups factors, covarying for depression, indicated a significant main effect of group, F(2, 70) = 3.37, p < .04, [eta]p2 = .09, on performance (see Figure 1). Again, there was a main effect of gender, F(1, 70) = 11.61, p < .01, [eta]p2 = .14; men performed better than women, but there was no interaction between gender and group. Adding education as an additional covariate in the analysis did not affect these results. Post hoc LSD tests indicated that OCD patients with high hoarding performed worse than both controls (p < .03, mean difference = -4.8, CI = -8.98, -0.66) and OCD patients with low-hoarding symptoms (p < .01, mean difference = -6.5, CI = -10.8, -2.21). These data confirm that in OCD patients the presence of hoarding symptoms (regardless of gender and comorbid depression) is associated with impaired decision making.

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Figure 1. Performance on the Iowa Gambling Task in controls (n = 38), obsessive–compulsive disorder (OCD) patients with low hoarding (n = 29), and OCD patients with high hoarding (n = 10). Values represent mean net score per 20 card selections (± SEM) from the advantageous (C + D) minus the disadvantageous (A + B) decks.
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To examine hoarding effects on the number of anticipatory SCR fluctuations, we carried out repeated measures ANOVA with deck (two levels) as the repeated measure, and group (controls, n = 24; patients with low hoarding, n = 18; and patients with high hoarding, n = 5) as the between-groups factor, covarying for depression. Full SCR data were only available for 5 of the 10 high hoarding OCD patients because of loss of SCR data for technical reasons. Results indicate significant between-groups differences in number of anticipatory fluctuations, F(2, 43) = 6.38, p < .01, [eta]p2 = .22 (see Figure 2). Post hoc LSD tests indicated that patients with low-hoarding symptoms showed a greater number of anticipatory fluctuations than controls (p < .02, mean difference = 0.17, CI = 0.033, 0.32) and patients with high hoarding (p < .01, mean difference = 0.37, CI = 0.14, 0.6). Patients with high hoarding showed a trend toward a lower number of anticipatory SCRs than controls (p = .085, mean difference = -0.195, CI = -0.42, 0.028). We also examined response SCRs using repeated-measures ANOVA, with deck (two levels) and reinforcement (two levels) as the repeated measures and hoarding group as the between-groups factor, covarying for depression. Results indicate a significant effect of reinforcement, F(1, 43) = 12.2, p < .01, [eta]p2 = .22; loss was greater than win, and there was a main effect of group, F(2, 43) = 5.80, p < .01, [eta]p2 = .21 (see Figure 2). Post hoc LSD tests revealed that OCD patients with high hoarding showed a smaller number of response fluctuations than controls (p < .02, mean difference = -0.43, CI = -0.783, -0.0814) and patients with low hoarding (p < .01, mean difference = -0.55, CI = -0.915, -0.193). In summary, OCD patients with low-hoarding symptoms showed enhanced autonomic responses during the anticipatory phase of the IGT, whereas OCD patients with high hoarding symptoms showed a smaller number of SCR fluctuations, particularly during the response phase of the IGT. These data suggest that OCD patients with low and high hoarding symptoms can be dissociated on the basis of their SCR and performance on the IGT, with patients with high hoarding showing poorer performance and fewer SCR fluctuations than controls and patients with low hoarding symptoms.

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Figure 2. Number of skin conductance response (SCR) fluctuations during Iowa Gambling Task performance in controls (n = 24) and obsessive–compulsive disorder patients with low (n = 18) and high hoarding (n = 5) symptoms. Values shown are mean (± SEM) number of SCR fluctuations generated prior to (anticipatory) or in response to (win/loss) card selections from the advantageous (C + D) and disadvantageous (A + B) decks.
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Set Shifting Performance on the WCST
Nonparametric between groups tests were carried out on the WCST data because of nonnormal distribution and unequal variance in the two groups. Mann–Whitney U tests indicated that patients completed fewer categories, made more perseverative errors, and made more nonperseverative errors than controls (see Table 3). These differences in performance are unlikely to have been because of between-groups differences in depression as there was no correlation between depression and WCST performance. However, as has been previously reported (Cavallaro et al., 2003), the number of years' education completed was negatively associated with number of perseverative errors (r = -.33, p < .04) and nonperseverative errors (r = -.41, p < .01) in OCD patients but not in controls. As controls were slightly more educated than OCD patients, we decided to examine the influence of education on the between-groups differences in WCST errors more carefully. Data for perseverative and nonperseverative errors were subjected to a log-10 (n + 1) transformation, as is recommended for positively skewed data with zeros (Tabachnick & Fidell, 1996), and univariate ANOVAs were carried out, covarying for education and depression. These tests confirmed that the covariate of education had a significant effect on the number of perseverative errors, F(1, 76) = 11.06, p < .01, [eta]p2 = .13, and nonperseverative errors, F(1, 76) = 14.49, p < .01, [eta]p2 = .17. The between-groups difference in perseverative errors was almost significant, F(1, 76) = 3.71, p < .06, [eta]p2 = .05, whereas the difference in nonperseverative errors became nonsignificant, F(1, 76) = 2.68, p = .10, [eta]p2 = .04, when the analysis covaried for education and depression.

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Table 3 Wisconsin Card Sorting Test (WCST) Results in Controls and Obsessive–Compulsive Disorder (OCD) Patients
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Relationship Between Set Shifting and OCD Symptoms
An exploratory stepwise multiple regression analysis was used to assess the relationship between OCD symptoms (as measured by the OCI–R), illness severity, depression, anxiety, age, education, verbal IQ, and each measure of WCST performance in OCD patients. These analyses suggested that poorer performance on the WCST was significantly associated with symmetry/order symptoms on the OCI–R. The number of categories completed showed an inverse relationship with symmetry/order symptoms (R2 = .15; [beta] = -.38, p < .03), and symmetry/order symptoms were positively associated with the number of perseverative errors (R2 = .13, [beta] = .36, p < .03). These relationships were independent of depression, state anxiety, total OCD severity, age, education, verbal IQ, and other OCD symptom dimensions (see Table 2). Nonperseverative errors on the WCST were negatively associated with years of education (R2 = .16; [beta] = -.40, p < .02). Taken together, these findings indicate that OCD patients performed more poorly on the WCST than controls. The data further suggest that symmetry/order symptoms contributed to the lower number of categories completed and the increased number of perseverative errors in OCD patients. The increased number of nonperseverative errors, however, appeared to be related to the lower educational attainment of OCD patients.
Discussion
In this study, we examined the influence of different OCD symptom dimensions on two aspects of neuropsychological function: (a) decision making, assessed with the IGT, and (b) attentional set shifting, measured with the WCST. The IGT and WCST are believed to measure dissociable cognitive functions associated predominantly, but not exclusively, with ventromedial and dorsolateral PFC function, respectively. Our findings suggest that OCD patients as a group do not show a decision making impairment on the IGT, but the presence and severity of hoarding symptoms in OCD is specifically associated with poorer decision making. In contrast to performance on the IGT, OCD patients as a group showed poorer set shifting on the WCST relative to controls; a finding influenced (but not fully explained) by lower education in OCD patients. Exploratory multiple regression analyses indicated that set shifting performance showed some negative association with symmetry/order symptoms in OCD patients, seemingly because of a greater number of perseverative errors. Our findings indicate that OCD symptom dimensions influence neuropsychological task performance in specific ways, and inconsistencies in previous studies (e.g., Cavedini, Riboldi, D'Annucci, et al., 2002, vs. Nielen et al., 2002) might be explained by differences in the representation of symptom dimensions in different groups of patients.
Decision Making and Hoarding Symptoms in OCD
OCD patients with high hoarding symptoms performed worse than OCD patients with low hoarding symptoms and healthy controls on the IGT, selecting more cards from the disadvantageous decks throughout the task. These findings strongly suggest a hoarding-related deficit in decision making, which supports other psychometric data showing that hoarders have problems making decisions (Frost & Gross, 1993; Frost & Shows, 1993; Steketee & Frost, 2003). Impairments in decision making could actually contribute to hoarding symptoms through an inability to decide which objects to keep and which to discard (Steketee & Frost, 2003). The current finding of a link between hoarding and risky behavior on the IGT may also be consistent with data suggesting a high prevalence of impulse-control disorders in OCD patients with hoarding symptoms (Winsberg et al., 1999) and with the reported high incidence of hoarding symptoms in heavy gamblers (Frost, Meagher, & Riskind, 2001).
The exploratory multiple regression analyses also pointed to a weaker association between washing symptoms and poor IGT performance, which was statistically independent from the association between IGT performance and hoarding symptoms, although this effect disappeared when male and female patients were examined separately. Further research will be required to clarify the symptom specificity of these findings. In addition to the effects of hoarding symptoms on IGT performance, our results show superior performance and enhanced explicit awareness of the correct strategy in men, replicating findings of Reavis and Overman (2001) and Overman et al. (2004). The current results also suggest that poor performers in this study were not aware of the winning strategy and so cannot be described as deliberate or “conscious” risk takers.
The observed disadvantageous pattern of responding in patients with high hoarding emerged alongside a reduction in the number of task-related SCR fluctuations. This was in contrast to OCD patients with low hoarding, who showed enhanced autonomic responses during decision making. A correlation analysis across all participants indicated that the number of fluctuations in response to a loss from the disadvantageous decks was related to total IGT score (r = .32, p < .03), so the reduction in SCRs in patients with high hoarding may have contributed to their poor task performance. Although the SCR findings in patients with high hoarding are based on a small sample (n = 5), and should therefore be regarded as preliminary, they suggest a reduction in somatic signals in OCD patients with high hoarding, which could contribute to their difficulties making decisions. The observed pattern of impaired IGT performance and a general reduction in task-related SCRs in patients with hoarding symptoms is similar to that seen in patients with amygdala lesions and some substance dependent individuals (Bechara & Damasio, 2002; Bechara et al., 1999). Although patients with ventromedial PFC lesions show poor decision making combined with significantly reduced anticipatory—but not response—SCRs, patients with amygdala lesions show blunted anticipatory and response SCRs (Bechara et al., 1999). Patients with amygdala lesions also fail to acquire conditioned SCRs to simple visual stimuli paired with a loud sound (Bechara et al., 1999). To determine whether patients with hoarding symptoms resemble patients with amygdala lesions, researchers conducting future experiments should replicate the current IGT and SCR findings in a larger sample and examine whether hoarders acquire conditioned SCRs. In addition, it would be interesting to see whether patients with high hoarding symptoms also show reduced autonomic reactivity, relative to controls and low hoarders, during a hoarding symptom-provocation paradigm (e.g., when participants are asked to look at pictures of commonly hoarded items and imagine throwing them away).
In terms of possible brain mechanisms underlying the hoarding-related deficit in decision making, we have already alluded to the possibility of amygdala dysfunction in patients with hoarding symptoms (see above). A number of neuroimaging and lesion studies also point to an important role for the right orbitofrontal and anterior cingulate cortices in mediating performance on decision making tasks (Bolla et al., 2003; Bolla, Eldreth, Matochik, & Cadet, 2004; Bush et al., 2002; Ernst et al., 2002; Rogers et al., 2004; Tranel, Bechara, & Denburg, 2002). We find it interesting that abnormalities in both brain regions have also been implicated in hoarding: Abnormal collecting behavior has been reported following lesions to the right mesial PFC (Anderson, Damasio, & Damasio, 2005), the right orbitofrontal cortex was activated in a functional magnetic resonance imaging study of hoarding-related symptom provocation (Mataix-Cols et al., 2004), and a recent positron emission tomography study revealed hypoactivity at rest in the dorsal anterior cingulate cortex in OCD patients with hoarding symptoms, relative to those without hoarding (Saxena et al., 2004). Anterior cingulate activation is also closely related to the generation of SCR fluctuations during risky decision making (Critchley, Mathias, & Dolan, 2001). Functional abnormalities in the right orbitofrontal and anterior cingulate cortices could therefore have contributed to the poorer task performance and lower number of SCRs seen in OCD patients with high hoarding relative to those with low hoarding.
Finally, the current data showing worse IGT performance in patients with high hoarding are potentially consistent with findings from Cavedini, Riboldi, D'Annucci, et al. (2002), indicating worse performance in patients who fail to respond to treatment with selective serotonin reuptake inhibitors (SSRIs). OCD patients with hoarding symptoms consistently show poorer response to pharmacotherapy (Black et al., 1998; Mataix-Cols et al., 1999; Winsberg et al., 1999), so it is possible that the low-treatment response sample in the study by Cavedini, Riboldi, D'Annucci, et al. (2002) contained a higher proportion of patients with hoarding symptoms. Future studies should examine the relationship between hoarding symptoms, decision making, right orbitofrontal and anterior cingulate cortex function, and treatment response in OCD patients.
The current results raise interesting questions about the nosological status of compulsive hoarding. Should it be regarded as a variant of OCD or a separate disorder that often co-occurs with OCD? On the one hand, hoarding symptoms are relatively common in OCD (but no more common than other non-OCD comorbid conditions; Steketee & Frost, 2003; Wu & Watson, 2005), and they correlate with other OCD symptoms (but no more strongly than with other non-OCD symptoms, e.g., depression; Tortella-Feliu et al., in press). On the other hand, several sources of evidence, including the current study, strongly suggest that patients with hoarding symptoms are different from nonhoarding OCD patients (Mataix-Cols et al., 2005). A multidimensional model of OCD has been proposed, which allows reconciling these “lumping” versus “splitting” approaches. Rather than a symptom of OCD or a completely different disorder, the “compulsive hoarding syndrome” could be better understood as a symptom dimension that can co-occur with other OCD symptom dimensions as well as other dimensions of psychopathology. According to this model, the most fruitful research strategy would be to focus on studying both the common and the specific etiological factors implicated in hoarding behavior (Mataix-Cols et al., 2005).
Set Shifting and Symmetry/Order Symptoms in OCD
Turning to set shifting performance, our data indicate that the symmetry/order dimension was moderately associated with poorer set shifting ability on the WCST. In particular, the regression analyses, though exploratory, suggested that symmetry/order symptoms were associated with a greater number of perseverative errors and a lower number of categories completed in OCD patients. These findings did not support our prediction that the checking/obsessions dimension, previously associated with abnormal dorsal prefrontal cortical and striatal activity (Mataix-Cols et al., 2004), would be more impaired at set shifting. However, it is plausible that checking and symmetry/order symptoms are linked via the presence of comorbid chronic tics or Tourette's syndrome. For example, Leckman et al. (1997) reported that patients with comorbid tics are more likely to endorse both checking and symmetry/order symptoms. Furthermore, Alsobrook, Leckman, Goodman, Rasmussen, and Pauls (1999) reported that the relatives of OCD probands with high scores on either the checking or the symmetry/order dimension were twice as likely to have a first-degree relative with OCD compared with individuals with low scores on these factors. Neuroimaging studies are now needed to examine the neural correlates of symmetry/order symptoms in OCD and determine how these compare with those of checking symptoms. Although the checking/obsessions dimension has previously been associated with abnormal activation in dorsal prefrontal cortical and striatal circuitry (Mataix-Cols et al., 2004), it is possible that a greater degree of functional impairment within this circuit may underlie the symmetry/order dimension. In the only published study that has examined the neural correlates of symmetry/order symptoms, Rauch et al. (1998) revealed that this dimension was associated with a trend toward reduced regional cerebral blood flow in the right caudate nucleus during a continuous performance task. Set shifting performance recruits the caudate nucleus (Monchi, Petrides, Petre, Worsley, & Dagher, 2001), and one study linked increased resting caudate function in OCD patients to deficits on the WCST (Lucey et al., 1997). These data raise the intriguing possibility that OCD patients, and in particular those with symmetry/order symptoms, show increased perseverative errors on the WCST because of abnormal striatal-based learning mechanisms. However, the results can also be interpreted as differences in cognitive style. Patients with symmetry/order symptoms may be focusing on aspects of the task that were not beneficial to performance, such as placing the cards in a particular place on the screen, selecting them in a specific sequence, or paying too much attention to changes in card design. Although selecting cards in a specific sequence would probably have resulted in a greater number of nonperseverative errors, attention to irrelevant detail in general may have distracted patients with symmetry/order symptoms, making them less aware of negative feedback and resulting in more perseverative errors, as observed.
Limitations
Results should be taken as preliminary because of the relatively small sample size, particularly for the SCR data. Furthermore, we report a large number of statistical analyses but have not corrected for multiple comparisons with a Bonferroni adjustment because this might be too conservative: We report data from two different neuropsychological tests, purported to assess different aspects of neuropsychological function and selected to answer specific a priori hypotheses, and have therefore regarded the data as “separate experiments” for statistical purposes, presenting results at p < .05 throughout. Because of the relatively large number of independent variables in the regression analyses, these results should be viewed as exploratory. These data are a novel exploration of the effects of OCD symptom dimensions on specific aspects of neuropsychological function and are intended to help shape future hypotheses and research questions. Findings should be replicated in larger samples of patients and healthy volunteers, with deliberate selection of patients with hoarding symptoms to confirm the current findings from the IGT. Controls were more educated than OCD patients in this sample, so future studies should take account of this by either matching or controlling for education. Most patients were medicated with SSRIs at the time of this study, although SSRIs are not thought to affect cognitive function in OCD (Mataix-Cols, Alonso, Pifarre, Menchon, & Vallejo, 2002). Most patients had comorbid conditions, but there were no differences between the high and low hoarding groups regarding comorbidity.
References
Alonso, P., Menchon, J. M., Pifarre, J., Mataix-Cols, D., Torres, L., & Salgado, P., et al. (2001). Long-term follow-up and predictors of clinical outcome in obsessive–compulsive patients treated with serotonin reuptake inhibitors and behavioral therapy. Journal of Clinical Psychiatry, 62, 535–540. [Context Link]
Alsobrook, J. P., II, Leckman, J. F., Goodman, W. K., Rasmussen, S. A., & Pauls, D. L. (1999). Segregation analysis of obsessive–compulsive disorder using symptom-based factor scores. American Journal of Medical Genetics, Neuropsychiatric Genetics, 88, 669–675. [Context Link]
American Psychiatric Association (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: Author. [Context Link]
Anderson, S. W., Damasio, H., & Damasio, A. R. (2005). A neural basis for collecting behaviour in humans. Brain, 128, 201–212. Buy Now Bibliographic Links [Context Link]
Anderson, S. W., Damasio, H., Jones, R. D., & Tranel, D. (1991). Wisconsin Card Sorting Test performance as a measure of frontal lobe damage. Journal of Clinical and Experimental Neuropsychology, 13, 909–922. [Context Link]
Basso, M. R., Bornstein, R. A., Carona, F., & Morton, R. (2001). Depression accounts for executive function deficits in obsessive–compulsive disorder. Neuropsychiatry, Neuropsychology, and Behavioural Neurology, 14, 241–245. [Context Link]
Bechara, A., Damasio, A. R., Damasio, H., & Anderson, S. W. (1994). Insensitivity to future consequences following damage to human prefrontal cortex. Cognition, 50, 7–15. Bibliographic Links [Context Link]
Bechara, A., & Damasio, H. (2002). Decision making and addiction: I. Impaired activation of somatic states in substance dependent individuals when pondering decisions with negative future consequences. Neuropsychologia, 40, 1675–1689. Bibliographic Links [Context Link]
Bechara, A., Damasio, H., Damasio, A. R., & Lee, G. P. (1999). Different contributions of the human amygdala and ventromedial prefrontal cortex to decision making. The Journal of Neuroscience, 19, 5473–5481. [Context Link]
Bechara, A., Damasio, H., Tranel, D., & Anderson, S. W. (1998). Dissociation of working memory from decision making within the human prefrontal cortex. The Journal of Neuroscience, 18, 428–437. [Context Link]
Bechara, A., Damasio, H., Tranel, D., & Damasio, A. R. (1997, February 28). Deciding advantageously before knowing the advantageous strategy. Science, 275, 1293–1295. [Context Link]
Bechara, A., Tranel, D., & Damasio, H. (2000). Characterization of the decision-making deficit of patients with ventromedial prefrontal cortex lesions. Brain, 123, 2189–2202. Bibliographic Links [Context Link]
Bechara, A., Tranel, D., Damasio, H., & Damasio, A. R. (1996). Failure to respond autonomically to anticipated future outcomes following damage to prefrontal cortex. Cerebral Cortex, 6, 215–225. Bibliographic Links [Context Link]
Beck, A. T., Ward, C., Mendelson, M., Mock, J., & Erbaugh, J. (1961). An inventory for measuring depression. Archives of General Psychiatry, 4, 561–571. Bibliographic Links [Context Link]
Black, D. W., Monahan, P., Gable, J., Blum, N., Clancy, G., & Baker, P. (1998). Hoarding and treatment response in 38 nondepressed subjects with obsessive–compulsive disorder. Journal of Clinical Psychiatry, 59, 420–425. Bibliographic Links [Context Link]
Bolla, K. I., Eldreth, D. A., London, E. D., Kiehl, K. A., Mouratidis, M., & Contoreggi, C., et al. (2003). Orbitofrontal cortex dysfunction in abstinent cocaine abusers performing a decision-making task. Neuroimage, 19, 1085–1094. [Context Link]
Bolla, K. I., Eldreth, D. A., Matochik, J. A., & Cadet, J. L. (2004). Sex-related differences in a gambling task and its neurological correlates. Cerebral Cortex, 14, 1226–1232. Buy Now Bibliographic Links [Context Link]
Bush, G., Vogt, B. A., Holmes, J., Dale, A. M., Greve, D., & Jenicke, M. A., et al. (2002). Dorsal anterior cingulate cortex: A role in reward-based decision making. Proceedings of the National Academy of Sciences, USA, 9, 523–528. [Context Link]
Cavallaro, R., Cavedini, P., Mistretta, P., Bassi, T., Angelone, S. M., & Ubbiali, A., et al. (2003). Basal-corticofrontal circuits in schizophrenia and obsessive–compulsive disorder: A controlled, double dissociation study. Biological Psychiatry, 54, 437–443. [Context Link]
Cavedini, P., Riboldi, G., D'Annucci, A., Belotti, P., Cisima, M., & Bellodi, L. (2002). Decision-making heterogeneity in obsessive–compulsive disorder: Ventromedial prefrontal cortex function predicts different treatment outcomes. Neuropsychologia, 40, 205–211. Bibliographic Links [Context Link]
Cavedini, P., Riboldi, G., Keller, R., D'Annucci, A., & Bellodi, L. (2002). Frontal lobe dysfunction in pathological gambling patients. Biological Psychiatry, 51, 334–341. Bibliographic Links [Context Link]
Clark, L., Manes, F., Antoun, N., Sahakian, B. J., & Robbins, T. W. (2003). The contributions of lesion laterality and lesion volume to decision-making impairment following frontal lobe damage. Neuropsychologia, 41, 1474–1483. Bibliographic Links [Context Link]
Cohen, J. (1998). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.
Critchley, H. D., Mathias, C. J., & Dolan, R. J. (2001). Neural activity in the human brain relating to uncertainty and arousal during anticipation. Neuron, 29, 537–545. Bibliographic Links [Context Link]
Damasio, A. R. (1994). Descartes' error: Emotion, reason, and the human brain. New York: Putnam. [Context Link]
Ernst, M., Bolla, K., Mouratidis, M., Contoreggi, C., Matochik, J. A., & Kurian, V., et al. (2002). Decision-making in a risk-taking task: A PET study. Neuropsychopharmacology, 26, 682–691. [Context Link]
First, M. B., Gibbon, M., Spitzer, R. L., Williams, J. B. W., & Smith, B. L. (1995). Structured Clinical Interview for DSM–IV Axis II Disorders—Patient Edition. New York: New York State Psychiatric Institute, Biometrics Research Department. [Context Link]
First, M. B., Spitzer, R. L., Gibbon, M., & Williams, J. B. W. (1995). Structured Clinical Interview for DSM–IV Axis I Disorders—Patient Edition. New York: New York State Psychiatric Institute, Biometrics Research Department. [Context Link]
Foa, E. B., Huppert, J. D., Leiberg, S., Langner, R., Kichic, R., & Hajcak, G., et al. (2002). The Obsessive–Compulsive Inventory: Development and validation of a short version. Psychological Assessment, 14, 485–496. [Context Link]
Frost, R. O., & Gross, R. C. (1993). The hoarding of possessions. Behaviour Research and Therapy, 31, 367–382. [Context Link]
Frost, R. O., Meagher, B. M., & Riskind, J. H. (2001). Obsessive–compulsive features in pathological lottery and scratch-ticket gamblers. Journal of Gambling Studies, 17, 5–19. [Context Link]
Frost, R. O., & Shows, D. L. (1993). The nature and measurement of compulsive indecisiveness. Behaviour Research and Therapy, 31, 683–692. [Context Link]
Frost, R. O., Steketee, G., & Grisham, J. (2004). Measurement of compulsive hoarding: Saving inventory revised. Behaviour Research and Therapy, 42, 1163–1182. [Context Link]
Goodman, W. K., Price, L. H., Rasmussen, S. A., Mazure, C., Delgado, P., & Heninger, G. R., et al. (1989). The Yale–Brown Obsessive–Compulsive Scale (Y-BOCS): I. Development, use, and reliability. Archives of General Psychiatry, 46, 1006–1011. [Context Link]
Grant, S., Contoreggi, C., & London, E. D. (2000). Drug abusers show impaired performance in a laboratory test of decision making. Neuropsychologia, 38, 1180–1187. Bibliographic Links [Context Link]
Heaton, R. K. (1981). Wisconsin Card Sorting Test manual (4th ed.). Odessa, FL: Psychological Assessment Resources. [Context Link]
Kuelz, A. K., Hohagen, F., & Voderholzer, U. (2004). Neuropsychological performance in obsessive–compulsive disorder: A critical review. Biological Psychology, 65, 185–236. Bibliographic Links [Context Link]
Leckman, J. F., Grice, D. E., Boardman, J., Zhang, H., Vitale, A., & Bondi, C., et al. (1997). Symptoms of obsessive–compulsive disorder. American Journal of Psychiatry, 154, 911–917. [Context Link]
Lucey, J. V., Burness, C. E., Costa, D. C., Gacinovic, S., Pilowsky, L. S., & Ell, P. J., et al. (1997). Wisconsin Card Sorting Task (WCST) errors and cerebral blood flow in obsessive–compulsive disorder (OCD). British Journal of Medical Psychology, 70, 403–411. [Context Link]
Mataix-Cols, D., Alonso, P., Pifarre, J., Menchon, J. M., & Vallejo, J. (2002). Neuropsychological performance in medicated vs. unmedicated patients with obsessive–compulsive disorder. Psychiatry Research, 109, 255–264. Bibliographic Links [Context Link]
Mataix-Cols, D., Marks, I. M., Greist, J. H., Kobak, K. A., & Baer, L. (2002). Obsessive–compulsive symptom dimensions as predictors of compliance with and response to behaviour therapy: Results from a controlled trial. Psychotherapy and Psychosomatics, 71, 255–262. [Context Link]
Mataix-Cols, D., Rauch, S. L., Baer, L., Eisen, J. L., Shera, D. M., & Goodman, W. K., et al. (2002). Symptom stability in adult obsessive–compulsive disorder: Data from a naturalistic two-year follow-up study. American Journal of Psychiatry, 159, 263–268. [Context Link]
Mataix-Cols, D., Rauch, S. L., Manzo, P. A., Jenike, M. A., & Baer, L. (1999). Use of factor-analyzed symptom dimensions to predict outcome with serotonin reuptake inhibitors and placebo in the treatment of obsessive–compulsive disorder. American Journal of Psychiatry, 156, 1409–1416. Bibliographic Links [Context Link]
Mataix-Cols, D., Rosario-Campos, M. C., & Leckman, J. F. (2005). A multidimensional model of obsessive–compulsive disorder. American Journal of Psychiatry, 162, 228–238. Bibliographic Links [Context Link]
Mataix-Cols, D., Wooderson, S., Lawrence, N., Brammer, M. J., Speckens, A., & Phillips, M. L. (2004). Distinct neural correlates of washing, checking, and hoarding symptom dimensions in obsessive–compulsive disorder. Archives of General Psychiatry, 61, 564–576. Bibliographic Links [Context Link]
Milner, B. (1963). Effects of different brain lesions on card sorting. Archives of Neurology, 9, 100–110. [Context Link]
Mitchell, D. G., Colledge, E., Leonard, A., & Blair, R. J. (2002). Risky decisions and response reversal: Is there evidence of orbitofrontal cortex dysfunction in psychopathic individuals? Neuropsychologia, 40, 2013–2022. [Context Link]
Monchi, O., Petrides, M., Petre, V., Worsley, K., & Dagher, A. (2001). Wisconsin card sorting revisited: Distinct neural circuits participating in different stages of the task identified by event-related functional magnetic resonance imaging. The Journal of Neuroscience, 21, 7733–7741. [Context Link]
Moritz, S., Birkner, C., Kloss, M., Jacobsen, D., Fricke, S., & Bothern, A., et al. (2001). Impact of comorbid depressive symptoms on neuropsychological performance in obsessive–compulsive disorder. Journal of Abnormal Psychology, 110, 653–657. [Context Link]
Nelson, H. E. (1982). National Adult Reading Test (NART): Test manual. Windsor, Berkshire, England: NFER-Nelson. [Context Link]
Nielen, M. M., Veltman, D. J., De Jong, R., Mulder, G., & Den Boer, J. A. (2002). Decision making performance in obsessive–compulsive disorder. Journal of Affective Disorders, 69, 257–260. Bibliographic Links [Context Link]
Overman, W. H., Frassrand, K., Ansel, S., Trawalter, S., Bies, B., & Redmond, A. (2004). Performance on the IOWA card task by adolescents and adults. Neuropsychologia, 42, 1838–1851. Bibliographic Links [Context Link]
Rauch, S. L., Dougherty, D. D., Shin, L. M., Alpert, N. M., Manzo, P., & Leahy, L., et al. (1998). Neural correlates of factor-analyzed OCD symptom dimensions: A PET study. CNS Spectrums, 3, 37–43. [Context Link]
Reavis, R., & Overman, W. H. (2001). Adult sex differences on a decision-making task previously shown to depend on the orbital prefrontal cortex. Behavioral Neuroscience, 115, 196–206. Ovid Full Text Bibliographic Links [Context Link]
Rogers, R. D., Ramnani, N., Mackay, C., Wilson, J. L., Jezzard, P., & Carter, C. S., et al. (2004). Distinct portions of anterior cingulate cortex and medial prefrontal cortex are activated by reward processing in separable phases of decision-making cognition. Biological Psychiatry, 55, 594–602. [Context Link]
Saxena, S., Brody, A. L., Maidment, K. M., Smith, A. C., Zohrabi, N., & Katz, E., et al. (2004). Cerebral glucose metabolism in obsessive–compulsive hoarding. American Journal of Psychiatry, 161, 1038–1048. [Context Link]
Saxena, S., Maidment, K. M., Vapnik, T., Golden, G., Rishwain, T., & Rosen, R. M., et al. (2002). Obsessive–compulsive hoarding: Symptom severity and response to multimodal treatment. Journal of Clinical Psychiatry, 63, 21–27. [Context Link]
Spielberger, C. D. (1983). State–Trait Anxiety Inventory for Adults: Manual. Palo Alto, CA: Consulting Psychologists Press. [Context Link]
Steketee, G., & Frost, R. (2003). Compulsive hoarding: Current status of the research. Clinical Psychology Review, 23, 905–927. [Context Link]
Suzuki, A., Hirota, A., Takasawa, N., & Shigemasu, K. (2003). Application of the somatic marker hypothesis to individual differences in decision making. Biological Psychology, 65, 81–88. Bibliographic Links [Context Link]
Tabachnick, B. G., & Fidell, L. S. (1996). Using multivariate statistics (3rd ed.). New York: HarperCollins. [Context Link]
Tortella-Feliu, M., Fullana, M. A., Caseras, X., Andion, O., Torrubia, R., & Mataix-Cols, D. (in press). Spanish version of the Saving Inventory—Revised: Adaptation, psychometric properties, and relationship to personality variables. Behavior Modification. [Context Link]
Tranel, D., Bechara, A., & Denburg, N. L. (2002). Asymmetric functional roles of right and left ventromedial prefrontal cortices in social conduct, decision-making, and emotional processing. Cortex, 38, 589–612. Bibliographic Links [Context Link]
Van Honk, J., Hermans, E. J., Putman, P., Montagne, B., & Schutter, D. J. (2002). Defective somatic markers in sub-clinical psychopathy. Neuroreport, 13, 1025–1027. Buy Now Bibliographic Links [Context Link]
Winsberg, M. E., Cassic, K. S., & Koran, L. M. (1999). Hoarding in obsessive–compulsive disorder: A report of 20 cases. Journal of Clinical Psychiatry, 60, 591–597. Bibliographic Links [Context Link]
Wu, K. D., & Watson, D. (2005). Hoarding and its relation to obsessive–compulsive disorder. Behaviour Research and Therapy, 43, 897–921. [Context Link]
Keywords: obsessive–compulsive disorder; symptom dimensions; hoarding; decision making; set shifting
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